Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline
文献类型： 外文期刊
作者： Qin, Jun 1 ; Wang, Fengmin 1 ; Zhao, Qingsong 1 ; Shi, Ainong 2 ; Zhao, Tiantian 1 ; Song, Qijian 3 ; Ravelombola, Waltram 4 ; An, Hongzhou 1 ; Yan, Long 1 ; Yang, Chunyan 1 ; Zhang, Mengchen 1 ;
作者机构： 1.Hebei Acad Agr & Forestry Sci, Cereal & Oil Crop Inst, Natl Soybean Improvement Ctr, Shijiazhuang Sub Ctr,North China Key Lab Biol & Ge, Shijiazhuang, Peoples R China
2.Univ Arkansas, Dept Hort, Fayetteville, AR 72701 USA
3.ARS, USDA, Soybean Genom & Improvement Lab, Beltsville, MD USA
4.Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX USA
关键词： Glycine max; genome-wide association study; genomic selection; genotyping by sequencing; protein content; single nucleotide polymorphism
期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）
ISSN： 1664-462X
年卷期： 2022 年 13 卷
页码：
收录情况： SCI
摘要： Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283-19315351) included 7 candidate genes and the Hap.X-AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.
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